Abstract: An energy management system for a high-voltage direct current (HVDC) transmission network includes a HVDC transmission line with first and second ends and a corresponding HVDC voltage. A first modular multilevel converter (MMC) is linked to the first end, interfaced with a first AC grid via a point of common coupling (PCC) that maintains a specific voltage. At the opposite end, a second MMC with its own voltage rating is connected, which in turn links to a second AC grid. This grid integrates renewable energy sources, comprising solar photovoltaic and wind farm systems, along with a battery energy storage system that includes a battery governed by a dedicated controller. The second PCC voltage is managed within the second AC grid. The system permits the second MMC to regulate the overall HVDC voltage, ensuring efficient energy distribution within the transmission network.

Abstract: A control system for a direct current (DC) microgrid is described. The control system includes a DC bus with a positive bus line and a negative bus line, DC battery circuits with a positive coupler connected to the positive bus line and a negative coupler connected to the negative bus line, switches including a control input terminal located in each DC battery circuit, a distributed generator circuit connected to the DC bus, a constant load power circuit connected to the DC bus, a computing device connected to each control input terminal, the DC bus, the distributed generator circuit and the constant load power circuit. The computing device includes an electric circuitry, a memory, and at least one processor to determine a DC bus voltage, determine the SoC of the DC battery circuits, generate PWM signals that control the DC bus voltage, and balance the SoC of the DC battery circuits.

Abstract: A device and method for fault management for an electric power distribution network incorporating intermittent generation sources. The method involves configuring multiple hyperparameters for a series of Convolutional Neural Networks (CNNs). A first CNN is trained using current signal imagery from phasor measuring units (PMUs) during fault conditions to classify faults. A second CNN is trained with signal images from PMUs captured during pre-fault and fault cycles for identifying fault sections. Similarly, a third CNN is trained using these images to determine the exact fault location. Once trained, the CNNs are employed sequentially. The first CNN classifies the fault, the second detects the fault section, and the third ascertains the fault location. Subsequently, a comprehensive fault management strategy is deployed.

Abstract: An energy management system for a high-voltage direct current (HVDC) transmission network includes a HVDC transmission line with first and second ends and a corresponding HVDC voltage. A first modular multilevel converter (MMC) is linked to the first end, interfaced with a first AC grid via a point of common coupling (PCC) that maintains a specific voltage. At the opposite end, a second MMC with its own voltage rating is connected, which in turn links to a second AC grid. This grid integrates renewable energy sources, comprising solar photovoltaic and wind farm systems, along with a battery energy storage system that includes a battery governed by a dedicated controller. The second PCC voltage is managed within the second AC grid. The system permits the second MMC to regulate the overall HVDC voltage, ensuring efficient energy distribution within the transmission network.

Abstract: A method and system for mitigating low-frequency oscillations of a power system network (PSN). The method includes receiving multiple data sets from the PSN, comprising values of terminal voltage, a real power, and a reactive power. The method further employs the multiple data sets to a fuzzy c-means clustering technique, a deep learning technique and a whale optimization algorithm to generate a pair of parameter values for a power system stabilizer controlling a steady-state of the power system network.

Abstract: A method and system for mitigating low-frequency oscillations of a power system network (PSN). The method includes receiving multiple data sets from the PSN, comprising values of terminal voltage, a real power, and a reactive power. The method further employs the multiple data sets to a fuzzy c-means clustering technique, a deep learning technique and a whale optimization algorithm to generate a pair of parameter values for a power system stabilizer controlling a steady-state of the power system network.

Abstract: A versatile intelligent fault diagnosis (IFD) method for a distribution grid integrating renewable energy resources is described. Advanced signal processing techniques extract useful features from recorded three-phase current signals, which are input to a multilayer perceptron neural networks (MLP-NN) to diagnose i.e., to detect, classify, identify the feeder branch, and locate the faults. Once a fault is detected, classified and located, a grid operator may adjust grid parameters or dispatch a technician to correct the fault. The IFD method is independent of load demand, renewable energy generation, and fault information (resistance and inception angle) uncertainties, as well as measurement noise.

Abstract: A versatile intelligent fault diagnosis (IFD) method for a distribution grid integrating renewable energy resources is described. Advanced signal processing techniques extract useful features from recorded three-phase current signals, which are input to a multilayer perceptron neural networks (MLP-NN) to diagnose i.e., to detect, classify, identify the feeder branch, and locate the faults. Once a fault is detected, classified and located, a grid operator may adjust grid parameters or dispatch a technician to correct the fault. The IFD method is independent of load demand, renewable energy generation, and fault information (resistance and inception angle) uncertainties, as well as measurement noise.